Design, modular synthesis and screening of 58 shape-diverse 3-D fragments

Fragment-based drug discovery is widely used in both academia and industry during the early stages of drug discovery. There is a growing interest in the design of 3-D fragments for inclusion in fragment libraries in order to increase chemical space coverage. We present herein the design and synthesis of 58 shape-diverse 3-D fragments that are prepared using just three modular synthetic methodologies. The 3-D fragments comprise a cyclic scaffold (cyclopentane, pyrrolidine, piperidine, tetrahydrofuran or tetrahydropyran) with one aromatic or heteroaromatic ring and possess properties within 'rule-of-three' fragment space. 3-D shape is assessed using principal moments of inertia analysis and conformational diversity is achieved by considering all conformations up to 1.5 kcal mol<sup>-1</sup> above the energy of the global minimum energy conformer. Due to the modular nature of the fragment syntheses, these 3-D fragments are synthetically-enabled for fragment elaboration follow-on work, a key design feature. This modular, shape-diverse 3-D fragment collection has delivered privileged starting points across a spectrum of targets. Fragments from the set have been crystallographically validated in the SARS-CoV-2 main protease (M<sup>pro</sup>) and the nonstructural protein 3 (Nsp3) (Mac1) as well as human glycosyltransferase MGATV, a major enzyme in the mammalian <i>N</i>-glycosylation pathway and a promoter of aggressive metastatic cancers, underscoring the breadth of biological space that can be explored.

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Publication Details

Journal
Chemical Science
Published
2025-01-01
DOI
https://doi.org/10.1039/d5sc05819h
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
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article

Design, modular synthesis and screening of 58 shape-diverse 3-D fragments

X. Wang, Thomas D. Downes, Claudia De Fusco, Stephen D. Roughley et al.
Chemical Science
Modular Robots and Swarm Intelligence
article

Design, modular synthesis and screening of 58 shape-diverse 3-D fragments

X. Wang, Thomas D. Downes, Claudia De Fusco, Stephen D. Roughley, Lewis R. Vidler, James D. Firth, Liang Wu, Hanna F. Klein, Amelia K. Gilio, Simon Jones, Gail L. Wrigley, Alison J.‐A. Woolford, Peter O’Brien, Roderick E. Hubbard, Maria Ann Whatton, David C. Blakemore, J.F. Darby
article en

Abstract

Fragment-based drug discovery is widely used in both academia and industry during the early stages of drug discovery. There is a growing interest in the design of 3-D fragments for inclusion in fragment libraries in order to increase chemical space coverage. We present herein the design and synthesis of 58 shape-diverse 3-D fragments that are prepared using just three modular synthetic methodologies. The 3-D fragments comprise a cyclic scaffold (cyclopentane, pyrrolidine, piperidine, tetrahydrofuran or tetrahydropyran) with one aromatic or heteroaromatic ring and possess properties within 'rule-of-three' fragment space. 3-D shape is assessed using principal moments of inertia analysis and conformational diversity is achieved by considering all conformations up to 1.5 kcal mol<sup>-1</sup> above the energy of the global minimum energy conformer. Due to the modular nature of the fragment syntheses, these 3-D fragments are synthetically-enabled for fragment elaboration follow-on work, a key design feature. This modular, shape-diverse 3-D fragment collection has delivered privileged starting points across a spectrum of targets. Fragments from the set have been crystallographically validated in the SARS-CoV-2 main protease (M<sup>pro</sup>) and the nonstructural protein 3 (Nsp3) (Mac1) as well as human glycosyltransferase MGATV, a major enzyme in the mammalian <i>N</i>-glycosylation pathway and a promoter of aggressive metastatic cancers, underscoring the breadth of biological space that can be explored.

Chemical ScienceVol. 16(42)
Pfizer (United States) (US), The Francis Crick Institute (GB), Eli Lilly (United Kingdom) (GB), Granta Design (United Kingdom) (GB), University of York (GB)
Openalex Percentile: Top 74%
Modular Robots and Swarm Intelligence
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